Vector columns

Supabase offers a number of different ways to store and query vectors within Postgres. If you prefer to use Python to store and query your vectors using collections, see Managing collections. If you want more control over vectors within your own Postgres tables or would like to interact with them using a different language like JavaScript, keep reading.

Vectors in Supabase are enabled via pgvector, a PostgreSQL extension for storing and querying vectors in Postgres. It can be used to store embeddings.

Usage#

Enable the extension#

Create a table to store vectors#

After enabling the vector extension, you will get access to a new data type called vector. The size of the vector (indicated in parenthesis) represents the number of dimensions stored in that vector.

create table documents (
  id serial primary key,
  title text not null,
  body text not null,
  embedding vector(1536)
);

In the above SQL snippet, we create a documents table with a column called embedding (note this is just a regular Postgres column - you can name it whatever you like). We give the embedding column a vector data type with 1536 dimensions. Change this to the number of dimensions used in your vector application. For example, if you are generating embeddings using OpenAI's text-embeddings-ada-002 model, you would set this number as 1536 since that model produces 1536 dimensions.

Storing a vector / embedding#

In this example we'll generate a vector using the OpenAI API client, then store it in the database using the Supabase JavaScript client.

const title = 'First post!'
const body = 'Hello world!'

// Generate a vector using OpenAI
const embeddingResponse = await openai.createEmbedding({
  model: 'text-embedding-ada-002',
  input: body,
})

const [{ embedding }] = embeddingResponse.data.data

// Store the vector in Postgres
const { data, error } = await supabase.from('documents').insert({
  title,
  body,
  embedding,
})

This example uses the JavaScript Supabase client, but you can modify it to work with any supported language library.

Querying a vector / embedding#

Similarity search is the most common use case for vectors. pgvector support 3 new operators for performing similarity search:

Choosing the right operator depends on your needs. If you are searching over OpenAI embeddings, OpenAI recommends using cosine similarity. For more information on how embeddings work and how they relate to each other, see What are Embeddings?.

Supabase client libraries like supabase-js connect to your Postgres instance via PostgREST. PostgREST does not currently support pgvector similarity operators, so we'll need to wrap our query in a Postgres function and call it via the rpc() method:

create or replace function match_documents (
  query_embedding vector(1536),
  match_threshold float,
  match_count int
)
returns table (
  id bigint,
  content text,
  similarity float
)
language sql stable
as $$
  select
    documents.id,
    documents.content,
    1 - (documents.embedding <=> query_embedding) as similarity
  from documents
  where 1 - (documents.embedding <=> query_embedding) > match_threshold
  order by similarity desc
  limit match_count;
$$;

This function takes a query_embedding argument and compares it to all other embeddings in the documents table. Each comparison returns a similarity score. If the similarity is greater than the match_threshold argument, it is returned. The number of rows returned is limited by the match_count argument.

Feel free to modify this method to fit the needs of your application. The match_threshold ensures that only documents that have a minimum similarity to the query_embedding are returned. Without this, you may end up returning documents that subjectively don't match. This value will vary for each application - you will need to perform your own testing to determine the threshold that makes sense for your app.

To execute the function from your client library, call rpc() with the name of your Postgres function:

const { data: documents } = await supabaseClient.rpc('match_documents', {
  query_embedding: embedding, // Pass the embedding you want to compare
  match_threshold: 0.78, // Choose an appropriate threshold for your data
  match_count: 10, // Choose the number of matches
})

In this example embedding would be another embedding you wish to compare against your table of pre-generated embedding documents. For example if you were building a search engine, every time the user submits their query you would first generate an embedding on the search query itself (using openai.createEmbedding()), then pass it into the above rpc() function to match.

Vectors and embedding can be used for much more than search. Learn more about embeddings at What are Embeddings?.

Indexes#

Once your vector table starts to grow, you will likely want to add an index to speed up queries. See Managing indexes to learn how vector indexes work and how to create them.